size <- 12. Later this will be the number of rows of the matrix.x <- rnorm( size ).x1 by adding (on average 10 times smaller) noise to x: x1 <- x + rnorm( size )/10.x and x1 should be close to 1.0: check this with function cor.x2 and x3 by adding (other) noise to x.size <- 12
x <- rnorm( size )
x1 <- x + rnorm( size )/10
cor( x, x1 )
[1] 0.9916216
x2 <- x + rnorm( size )/10
x3 <- x + rnorm( size )/10
x1, x2 and x3 column-wise into a matrix using m <- cbind( x1, x2, x3 ).m.m.heatmap( m, Colv = NA, Rowv = NA, scale = "none" ).m <- cbind( x1, x2, x3 )
class( m )
[1] "matrix"
head( m )
x1 x2 x3
[1,] 1.45189199 1.54775918 1.4042768
[2,] 1.92957913 2.12452957 2.0461228
[3,] -0.05296283 -0.05628309 -0.1891822
[4,] -0.85348760 -0.63481616 -0.9733284
[5,] 1.31886756 1.34296340 1.4122569
[6,] 1.56697580 1.57278005 1.3745820
heatmap( m, Colv = NA, Rowv = NA, scale = "none" ) # high is dark red, low is yellow
# x1, x2, x3 follow similar color pattern, they should be correlated
y1…y4 (but not correlated with x), of the same length size.m from columns x1…x3,y1…y4 in some random order.y <- rnorm( size )
y1 <- y + rnorm( size )/10
y2 <- y + rnorm( size )/10
y3 <- y + rnorm( size )/10
y4 <- y + rnorm( size )/10
m <- cbind( y4, y3, x2, y1, x1, x3, y2 )
heatmap( m, Colv = NA, Rowv = NA, scale = "none" ) # high is dark red, low is yellow
cc <- cor( m ) to build the matrix of correlation coefficients between columns of m.round( cc, 3 ) to show this matrix with 3 digits precision.cc <- cor( m )
round( cc, 3 ) #
y4 y3 x2 y1 x1 x3 y2
y4 1.000 0.998 -0.240 0.990 -0.292 -0.314 0.998
y3 0.998 1.000 -0.217 0.988 -0.269 -0.287 0.997
x2 -0.240 -0.217 1.000 -0.284 0.988 0.986 -0.251
y1 0.990 0.988 -0.284 1.000 -0.324 -0.360 0.990
x1 -0.292 -0.269 0.988 -0.324 1.000 0.981 -0.307
x3 -0.314 -0.287 0.986 -0.360 0.981 1.000 -0.329
y2 0.998 0.997 -0.251 0.990 -0.307 -0.329 1.000
heatmap( cc, symm = TRUE, scale = "none" )
# E.g. value for (row: x1, col: y1) is the corerlation of vectors x1, y1.
# Values of 1.0 are on the diagonal: e.g. x1 is perfectly correlated with x1.
# Correlations between x, x vectors are close to 1.0.
# Correlations between y, y vectors are close to 1.0.
# Correlations between x, y vectors are close to 0.0.
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